Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation

• URL: http://arxiv.org/abs/2410.06893v1
• Date: Wed, 9 Oct 2024 13:57:39 GMT
• Title: Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation
• Authors: Seungho Lee, Hwijeong Lee, Hyunjung Shim,
• Abstract summary: Two main issues in low-budget SSLS are the poor-quality pseudo-labels for unlabeled data, and the performance drops. We propose a proximity-based label estimation, which generates highly accurate pseudo-labels for unlabeled data. Experimental results demonstrate remarkable performance in low-budget settings.
• Score: 17.151511119485246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the challenges of the semi-supervised LiDAR segmentation (SSLS) problem, particularly in low-budget scenarios. The two main issues in low-budget SSLS are the poor-quality pseudo-labels for unlabeled data, and the performance drops due to the significant imbalance between ground-truth and pseudo-labels. This imbalance leads to a vicious training cycle. To overcome these challenges, we leverage the spatio-temporal prior by recognizing the substantial overlap between temporally adjacent LiDAR scans. We propose a proximity-based label estimation, which generates highly accurate pseudo-labels for unlabeled data by utilizing semantic consistency with adjacent labeled data. Additionally, we enhance this method by progressively expanding the pseudo-labels from the nearest unlabeled scans, which helps significantly reduce errors linked to dynamic classes. Additionally, we employ a dual-branch structure to mitigate performance degradation caused by data imbalance. Experimental results demonstrate remarkable performance in low-budget settings (i.e., <= 5%) and meaningful improvements in normal budget settings (i.e., 5 - 50%). Finally, our method has achieved new state-of-the-art results on SemanticKITTI and nuScenes in semi-supervised LiDAR segmentation. With only 5% labeled data, it offers competitive results against fully-supervised counterparts. Moreover, it surpasses the performance of the previous state-of-the-art at 100% labeled data (75.2%) using only 20% of labeled data (76.0%) on nuScenes. The code is available on https://github.com/halbielee/PLE.

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